Not quite fitting the data using lsqcurvefit
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Hello everyone. I'm trying to use lsqcurvefit to get optimal parameter value for K (see code). The fitting looks fine except for 2 things: fitted curves are always shifted down by some amount to respect to the data (see picture), and the values I get for K are complex when it should be a real number (but I guess that's a subproduct of the main problem here). K values for different initial guesses are similar, which is good. I really don't know what is going on, I hope someone can give me a hint.
Here's the code:
R=[0.1:0.1:0.4 0.6:0.2:1.8 2.1 2.4:0.4:3.2 3.8 4.8 6 8.4 12.8 20 36];
pks_locs1 = [114 93.5 144 167 199.5 225.5 275 271.5 282.5 301.5 315.5 319.5 348 356.5 362.5 390.5 408.5 420.5 464.5 449.5 457.5 477 494.5]';
CIS_H2 = 352.6;
H=0.0002529;
G=H./R';
fun_H2=@(K,R) pks_locs1(1)+CIS_H2*(K*G*(1+R)+1-sqrt((K*G*(1+R)+1)^2-R'*(2*K*G').^2))/(2*K*G');
K0_H2=100;
K=lsqcurvefit(fun_H2,K0_H2,R,pks_locs1(2:end))
plot([0 R],pks_locs1,'ko',R,fun_H2(K,R),'b-')
legend('Data','Fitted exponential')
title('Data and Fitted Curve')
The value I get for K is 9.3155e+03 - 1.1463e-01i. I have tried using lsqnonlin with similar results: fitted curve down-shifted and complex K values.
Thanks in advance!
6 个评论
Walter Roberson
2019-3-17
You have
fun_H2=@(K,R) pks_locs1(1)+CIS_H2*(K*G*(1+R)+1-sqrt((K*G*(1+R)+1)^2-R'*(2*K*G').^2))/(2*K*G');
Notice this includes sqrt((K*G*(1+R)+1)^2) . But your R is a vector, so the ^2 is being applied to a vector, unless the algebraic matrix multiplication by G collapses the vector (1+R) into a scalar. ^ is matrix exponential, not element-by-element exponential. * is algebraic matrix multiplication, not element-by-element multiplication. And further down in the expression you have /(2*K*G') where G is a vector, so the / is matrix division, not element-by-element division.
You should be using .* and .^ and ./ everywhere unless you deliberately want the values at one location to influence the calculation of values at all locations. The / operator is essentially a fitting operation rather than a division: if you want fitting to be taking place there then you should comment heavily .
采纳的回答
Matt J
2019-3-17
When your model function is fully vectorized, as suggested by Walter, the results are better, but only you can know for sure what your model function is supposed to be.
R=[0.1:0.1:0.4 0.6:0.2:1.8 2.1 2.4:0.4:3.2 3.8 4.8 6 8.4 12.8 20 36].';
pks_locs1 = [114 93.5 144 167 199.5 225.5 275 271.5 282.5 301.5 315.5 319.5 348 356.5 362.5 390.5 408.5 420.5 464.5 449.5 457.5 477 494.5]';
CIS_H2 = 352.6;
H=0.0002529;
G=H./R;
fun_H2= @(K,R)pks_locs1(1)+CIS_H2.*(K.*G.*(1+R)+1-sqrt((K.*G.*(1+R)+1).^2-R.*(2.*K.*G).^2))./(2.*K.*G);
K0_H2=5.0758e+04;
[K,fval]=lsqcurvefit(fun_H2,K0_H2,R,pks_locs1(2:end))
plot([0 R.'],pks_locs1,'ko',R,fun_H2(K,R),'b-')
legend('Data','Fitted exponential','location','southeast')
title('Data and Fitted Curve')
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